This patent application describes systems and methods for generating one or more predictors of osteoarthritis (OA) progression. Such predictors can be used, for example, in clinical settings to identify those individuals having an increased risk of OA progression. More specifically, this patent application describes systems and methods which use fractal signature analysis (FSA) to generate such predictors. Example systems and methods for using the predictors are also described herein.
Osteoarthritis (OA) is the leading cause of disability among persons aged 18 years and older. Currently, a total of 40 million Americans (two-thirds of whom are younger than 65), and 450 million individuals worldwide, are affected by arthritis. Direct medical costs are 81 billion dollars in the United States. More than half of all arthritis is due to OA. By the year 2030, the number of people with arthritis is expected to rise to 75 million; the majority of this rise is due to OA, the most common arthritis with aging that is increasing in prevalence due to the aging and increasing obesity of the population.
OA progression can be defined anatomically by means of plain radiographs, clinically by means of symptoms, or physiologically by means of a functional assessment. Of these three methods of defining OA progression, the anatomical means of assessment has prevailed. The only method currently accepted by regulators for evaluating disease progression in knee OA is the sequential radiographic assessment of joint space narrowing (JSN). Problems with radiographic evaluation of OA include difficulty reproducing patient position in order to measure joint space width, and relative insensitivity to change that requires large studies of 18 to 24 months duration to demonstrate changes. Further, changes in joint space width are confounded by meniscal damage and extrusion, which are also seen in OA. Risk factors such as body mass index (BMI), age, and gender are commonly used in OA clinical trials in an attempt to select individuals with greater risk of knee OA progression. However, the effect or interaction of these predictors is not well understood and they have not been highly successful. The continued lack of a good predictor has stalled pursuit of treatments for a disease that affects nearly twenty percent of the population and has a significant impact on productivity and quality of life.
Analyses of bone in OA date back over more than half a century and have provided clear indications that changes in periarticular bone occur very early in OA development. The bone architecture on radiographic images of osteoarthritic joints began to be analyzed in the 1990's by Buckland-Wright and colleagues using fractal signature analysis (FSA), a technique first applied in medicine to the study of abnormalities of lung radiographs. FSA evaluates the complexity of detail of an image (in this case a 2-dimensional image constituting a projection of the 3-D bone architecture) at a variety of scales spanning the typical size range of trabeculae (100-300 micrometers) and trabecular spaces (200-2000 micrometers). As described by Buckland-Wright and colleagues, the complexity of detail quantified by fractal dimension is determined principally by the number, spacing, and cross-connectivity of trabeculae. By nuclear magnetic resonance (NMR), another group has determined that the apparent fractal dimension is an index of bone marrow space pore size; pore size is in turn related to, and increases with, perforation and disappearance of trabeculae.
To date, fractal analysis has been applied successfully to the study of osteoporosis and arthritis of the spine, hips, pre- and post-joint replacement knees, anterior cruciate ligament ruptured knees, wrist, and hands. Plain radiographs have been used primarily, but the fractal analysis method is amenable to use of other image types such as those acquired by computed tomography and NMR.
One advantage of FSA is that it is robust to many of the pitfalls inherent in the gold standard measure of radiographic progression, joint space narrowing. Joint space narrowing is problematic due to the need for high quality images (often beyond the general quality of clinical images) using well-controlled acquisition protocols for extraction of good quantitative data. In particular, FSA has been shown to be robust to varying radiographic exposure, to changing pixel size, and knee repositioning.
To date, three studies have evaluated tibial cancellous bone changes longitudinally in the context of knee OA progression using FSA, but results have been conflicting. The first, a study of 240 patients reported in abstract form only, revealed significant differences in the pattern of FSA change (increased vertical FSA of most trabecular sizes and decreased horizontal FSA of large trabeculae) over 12 months between patients with slow (n=240) versus marked (n=12) joint space narrowing; these results were interpreted as indicative of local subchondral bone loss coincident with knee OA progression. A second much smaller study (n=40) failed to identify significant differences in the pattern of FSA change over the course of 24 months in slow and fast progressors. A third study evaluated FSA change over 3 years in one-third (n=400) of patients in a placebo-controlled trial of a bisphosphonate for knee OA. Compared with patients with non-rapid joint space narrowing (JSN), patients with rapid JSN tended to have a greater decrease in the vertical fractal dimensions (interpreted as a greater loss of most sizes of vertical trabeculae), and no significant difference in the horizontal trabeculae. By contrast, the non-progressor group showed a slight decrease in fractal dimensions for vertical and horizontal trabeculae over time and no drug treatment effect. The JSN progressors showed a marked and dose-dependent change in FSA with drug treatment consistent with a preservation of trabecular structure and reversal of the pathological changes with increasing drug dose.
The example systems and methods described in this patent application employ FSA for predicting OA progressors (e.g., for knees) using a generalized “shape analysis” of data that enables creation of an overall model which is predictive of OA progression independent of other non-radiographic variables.
In fractal signature data, the compression (vertical trabecular) and tension (horizontal trabecular) fractal dimension measures are calculated over a range of radii. The trends of compression and tension change over radius are modeled with polynomial (e.g., second order) multiple regression models. Covariates such as age, gender, BMI may be incorporated as well. The statistical correlations between clinical observations from the same individual are estimated with generalized linear models (GLM) and/or generalized estimation equations (GEE). The estimated regression coefficients are calculated for each individual from the model parameter estimates, and used in a second GLM/GEE model to generate a statistical score representing osteoarthritis progression-risk status.
Receiver operating characteristic (ROC) curves are generated based on the statistical scores using cross-validations. In the cross-validation, data are divided randomly into 5 folds, 4 folds are used to build the model and the remaining 1 fold is used to validate the model parameters.
Using the above-described approach, osteoarthritis progression over time, defined by joint space narrowing (JSN) has been found to be significantly associated with baseline fractal signatures. The regression coefficients estimated from the multiple regressions can predict the OA progression, independent of other covariates (age, gender, body mass index (BMW. This approach can be used, by way of example and without limitation, to power an OA treatment trial using more rapid progressors to thereby decrease the number of trial participants needed to show an effect, which in turn, reduces costs and drug exposure.